This plot shows the pipeline performance overview. Some metrics are calculated.

• Heatmap score[Contaminants]: as fraction of summed intensity with 0 = sample full of contaminants; 1 = no contaminants
• Heatmap score[Pep Intensity (>23.0)]: Linear scale of the median intensity reaching the threshold, i.e. reaching 221 of 223 gives score 0.25.
• Heatmap score[Charge]: Deviation of the charge 2 proportion from a representative Raw file.
• Heatmap score [MC]: the fraction (0% - 100%) of fully cleaved peptides per Raw file
• Heatmap score [MC Var]: each Raw file is scored for its deviation from the ‘average’ digestion state of the current study.
• Heatmap score [ID rate over RT]: Scored using ‘Uniform’ scoring function.
• Heatmap score [MS2 Oversampling]: The percentage of non-oversampled 3D-peaks.
• Heatmap score [Pep Missing]: Linear scale of the fraction of missing peptides.

This plot shows the Proteomics Experimental Design. You can see details about it in https://abibuilder.informatik.uni-tuebingen.de/archive/openms/Documentation/release/latest/html/classOpenMS_1_1ExperimentalDesign.html

This plot shows the ProteomicsLFQ pipeline summary statistics

This plot shows the ProteomicsLFQ pipeline final result. Including Sample Name、Possible Study Variables、identified the number of peptide in the pipeline、 and identified the number of modified peptide in the pipeline, eg. All data in this table are obtained from the out_msstats file. You can also remove the decoy with the remove_decoy parameter.

This statistic is extracted from the out_msstats file. Proteins supported by more peptide identifications can constitute more confident results.

The quantification information of peptides is obtained from the pep table in the mzTab file. The table shows the quantitative level of peptides in different study variables.

This table fully displays the peptide spectrum matching information in the mzTab file:

• sequence: peptide sequence
• unique
• search_engine_score

This table shows the changes in the number of spectra corresponding to each input file during the pipeline operation. And the number of peptides finally identified is obtained from the PSM table in the mzTab file. You can also remove the decoy with the remove_decoy parameter.:

• MS1_Num: The number of MS1 spectra extracted from mzMLs
• MS2_Num: The number of MS2 spectra extracted from mzMLs
• MSGF: The Number of spectra identified by MSGF search engine
• Comet: The Number of spectra identified by Comet search engine
• Final result of spectra: extracted from PSM table in mzTab file
• Final result of Peptides: extracted from PSM table in mzTab file

This information can be used to identify potential ionization problems including many 1+ charges from an ESI ionization source or an unexpected distribution of charges. MALDI experiments are expected to contain almost exclusively 1+ charged ions. An unexpected charge distribution may furthermore be caused by specific search engine parameter settings such as limiting the search to specific ion charges.

Generally, one should expect to have a high number of low intensity noise peaks with a low number of high intensity signal peaks. A disproportionate number of high signal peaks may indicate heavy spectrum pre-filtering or potential experimental problems. In the case of data reuse this plot can be useful in identifying the requirement for pre-processing of the spectra prior to any downstream analysis. The quality of the identifications is not linked to this data as most search engines perform internal spectrum pre-processing before matching the spectra. Thus, the spectra reported are not necessarily pre-processed since the search engine may have applied the pre-processing step internally. This pre-processing is not necessarily reported in the experimental metadata.

Mass deltas close to zero reflect more accurate identifications and also that the reporting of the amino acid modifications and charges have been done accurately. This plot can highlight systematic bias if not centered on zero. Other distributions can reflect modifications not being reported properly. Also it is easy to see the different between the target and the decoys identifications.